CN116128835A - Point cloud analysis-based power transmission wire sag measurement method, device and equipment - Google Patents
Point cloud analysis-based power transmission wire sag measurement method, device and equipment Download PDFInfo
- Publication number
- CN116128835A CN116128835A CN202310051465.9A CN202310051465A CN116128835A CN 116128835 A CN116128835 A CN 116128835A CN 202310051465 A CN202310051465 A CN 202310051465A CN 116128835 A CN116128835 A CN 116128835A
- Authority
- CN
- China
- Prior art keywords
- point cloud
- power transmission
- semantic segmentation
- transmission wire
- point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000005540 biological transmission Effects 0.000 title claims abstract description 279
- 238000004458 analytical method Methods 0.000 title claims abstract description 30
- 238000000691 measurement method Methods 0.000 title abstract description 5
- 230000011218 segmentation Effects 0.000 claims abstract description 157
- 238000012545 processing Methods 0.000 claims abstract description 75
- 238000005070 sampling Methods 0.000 claims abstract description 58
- 238000000034 method Methods 0.000 claims abstract description 41
- 238000000513 principal component analysis Methods 0.000 claims abstract description 26
- 238000004590 computer program Methods 0.000 claims abstract description 23
- 239000011159 matrix material Substances 0.000 claims description 18
- 239000000725 suspension Substances 0.000 claims description 17
- 238000005259 measurement Methods 0.000 claims description 13
- 230000007246 mechanism Effects 0.000 claims description 13
- 238000000354 decomposition reaction Methods 0.000 claims description 9
- 239000004020 conductor Substances 0.000 claims description 5
- 230000002708 enhancing effect Effects 0.000 claims description 4
- 238000004891 communication Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000012847 principal component analysis method Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 229910021389 graphene Inorganic materials 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
- G01C11/04—Interpretation of pictures
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C3/00—Measuring distances in line of sight; Optical rangefinders
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/7715—Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/70—Labelling scene content, e.g. deriving syntactic or semantic representations
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02G—INSTALLATION OF ELECTRIC CABLES OR LINES, OR OF COMBINED OPTICAL AND ELECTRIC CABLES OR LINES
- H02G1/00—Methods or apparatus specially adapted for installing, maintaining, repairing or dismantling electric cables or lines
- H02G1/02—Methods or apparatus specially adapted for installing, maintaining, repairing or dismantling electric cables or lines for overhead lines or cables
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Electromagnetism (AREA)
- Geometry (AREA)
- Quality & Reliability (AREA)
- Image Analysis (AREA)
- Electric Cable Installation (AREA)
Abstract
The application relates to the technical field of power transmission, and provides a power transmission wire sag measurement method, a device, computer equipment, a storage medium and a computer program product based on point cloud analysis. The method and the device can improve accuracy and efficiency of determining sag of the power transmission wire. The method comprises the following steps: the method comprises the steps of obtaining point clouds of a power transmission line, wherein the power transmission line at least comprises two adjacent towers and power transmission wires hung on the two towers, conducting semantic segmentation processing on the point clouds by utilizing a pre-built point cloud semantic segmentation model to obtain semantic segmentation results of the point clouds, wherein the semantic segmentation results are used for representing power transmission wire point clouds corresponding to the power transmission wires in the point clouds, conducting principal component analysis processing on the power transmission wire point clouds according to the semantic segmentation results, determining two hanging points of the power transmission wires on the two adjacent towers in the point clouds, and determining sag of the power transmission wires by means of distances between sampling points on connecting lines of the two hanging points and the power transmission wire point clouds.
Description
Technical Field
The present application relates to the field of power transmission technologies, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for measuring sag of a power transmission wire based on point cloud analysis.
Background
With the development of power transmission technology, how to safely transmit power becomes an important research direction. Before the overhead line construction, a technician calculates the standard sag of a certain power transmission line according to relevant standards, and then the overhead line construction is carried out, however, due to errors in construction operation or due to factors such as time, climate, stress, damage, faults and the like, the actual sag of the power transmission line often has a certain deviation from the calculated standard sag, and the deviation becomes a power transmission line defect after exceeding the range, thereby threatening the power transmission safety.
The conventional technology usually determines the sag of the power transmission wire by means of manual field measurement, but the sag of the power transmission wire is easy to deviate from a measurement result due to the proficiency of a worker to a measuring instrument in the measurement process, so that the accuracy of determining the sag of the power transmission wire is low.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, a computer readable storage medium and a computer program product for sag measurement of a power transmission wire based on point cloud analysis.
In a first aspect, the present application provides a method for measuring sag of a power transmission wire based on point cloud analysis. The method comprises the following steps:
acquiring a point cloud of a power transmission line; the power transmission line at least comprises two adjacent towers and a power transmission wire hung on the two towers;
carrying out semantic segmentation processing on the point cloud by utilizing a pre-constructed point cloud semantic segmentation model to obtain a semantic segmentation result of the point cloud; the semantic segmentation result is used for representing a power transmission wire point cloud corresponding to the power transmission wire in the point cloud;
according to semantic segmentation results, carrying out principal component analysis processing on the point cloud of the power transmission wire, and determining two hanging points of the power transmission wire on two adjacent towers in the point cloud;
and determining sag of the power transmission wire through the distance between the sampling points on the connecting lines of the two hanging points and the power transmission wire point cloud.
In one embodiment, according to a semantic segmentation result, principal component analysis processing is performed on a point cloud of a power transmission wire, and two suspension points of the power transmission wire on two adjacent towers in the point cloud are determined, including:
according to the semantic segmentation result, carrying out principal component analysis processing on the power transmission wire point cloud to obtain a principal direction vector of the power transmission wire point cloud;
and determining two suspension points of the power transmission wire on two adjacent towers in the point cloud according to the coordinate information of the main direction vector of the power transmission wire point cloud.
In one embodiment, according to a semantic segmentation result, performing principal component analysis processing on a power transmission wire point cloud to obtain a principal direction vector of the power transmission wire point cloud, including:
according to the semantic segmentation result, calculating a covariance matrix corresponding to the point cloud of the transmission line;
and determining a main direction vector of the point cloud of the power transmission wire by carrying out singular value decomposition processing on the covariance matrix.
In one embodiment, performing semantic segmentation processing on a point cloud by using a pre-constructed point cloud semantic segmentation model to obtain a semantic segmentation result of the point cloud, including:
enhancing the connection feature in the point cloud by using a point cloud attention mechanism unit in the point cloud semantic segmentation model;
and carrying out semantic segmentation processing on the point cloud according to the features of the connection part to obtain a semantic segmentation result.
In one embodiment, the transmission line comprises two towers, a transmission line, ground, vegetation and a building;
carrying out semantic segmentation processing on the point cloud by utilizing a pre-constructed point cloud semantic segmentation model to obtain a semantic segmentation result of the point cloud, wherein the semantic segmentation result comprises the following steps:
carrying out semantic segmentation processing on the point cloud by utilizing a point cloud semantic segmentation model, and dividing the point cloud into a transmission wire point cloud, a tower point cloud, a ground point cloud, a vegetation point cloud and a building point cloud;
and determining a semantic segmentation result according to the transmission wire point cloud, the tower point cloud, the ground point cloud, the vegetation point cloud and the building point cloud.
In one embodiment, determining sag of the power transmission line from a distance between a sampling point on a connection line of two suspension points and a power transmission line point cloud includes:
identifying the distance between the sampling point and the power transmission wire point cloud to obtain a distance set;
and selecting the maximum distance from the distance set as the sag of the power transmission wire.
In one embodiment, before identifying the distance between the sampling point and the power transmission wire point cloud to obtain the distance set, the method further includes:
according to the preset interval, a plurality of sampling points are determined on the connecting line of the two hanging points;
calculating the distance between the sampling point and the power transmission wire point cloud to obtain a distance set, wherein the distance set comprises the following steps:
determining intersection points of extension lines of all sampling points along a ground normal vector and the point cloud of the transmission wire;
and calculating the distance between each sampling point and the corresponding intersection point to obtain a distance set.
In a second aspect, the application further provides a power transmission wire sag measurement device based on point cloud analysis. The device comprises:
the point cloud acquisition module is used for acquiring the point cloud of the power transmission line; the power transmission line at least comprises two adjacent towers and a power transmission wire suspended on the two towers;
the result obtaining module is used for carrying out semantic segmentation processing on the point cloud by utilizing a pre-constructed point cloud semantic segmentation model to obtain a semantic segmentation result of the point cloud; the semantic segmentation result is used for representing a power transmission wire point cloud corresponding to the power transmission wire in the point cloud;
the point cloud analysis module is used for carrying out principal component analysis processing on the point cloud of the power transmission wire according to the semantic segmentation result and determining two hanging points of the power transmission wire on the two adjacent towers in the point cloud;
and the sag determination module is used for determining sag of the power transmission wire through the distance between the sampling point on the connecting line of the two hanging points and the power transmission wire point cloud.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring a point cloud of a power transmission line; the power transmission line at least comprises two adjacent towers and a power transmission wire hung on the two towers; carrying out semantic segmentation processing on the point cloud by utilizing a pre-constructed point cloud semantic segmentation model to obtain a semantic segmentation result of the point cloud; the semantic segmentation result is used for representing a power transmission wire point cloud corresponding to the power transmission wire in the point cloud; according to semantic segmentation results, carrying out principal component analysis processing on the point cloud of the power transmission wire, and determining two hanging points of the power transmission wire on two adjacent towers in the point cloud; and determining sag of the power transmission wire through the distance between the sampling points on the connecting lines of the two hanging points and the power transmission wire point cloud.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring a point cloud of a power transmission line; the power transmission line at least comprises two adjacent towers and a power transmission wire hung on the two towers; carrying out semantic segmentation processing on the point cloud by utilizing a pre-constructed point cloud semantic segmentation model to obtain a semantic segmentation result of the point cloud; the semantic segmentation result is used for representing a power transmission wire point cloud corresponding to the power transmission wire in the point cloud; according to semantic segmentation results, carrying out principal component analysis processing on the point cloud of the power transmission wire, and determining two hanging points of the power transmission wire on two adjacent towers in the point cloud; and determining sag of the power transmission wire through the distance between the sampling points on the connecting lines of the two hanging points and the power transmission wire point cloud.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
acquiring a point cloud of a power transmission line; the power transmission line at least comprises two adjacent towers and a power transmission wire hung on the two towers; carrying out semantic segmentation processing on the point cloud by utilizing a pre-constructed point cloud semantic segmentation model to obtain a semantic segmentation result of the point cloud; the semantic segmentation result is used for representing a power transmission wire point cloud corresponding to the power transmission wire in the point cloud; according to semantic segmentation results, carrying out principal component analysis processing on the point cloud of the power transmission wire, and determining two hanging points of the power transmission wire on two adjacent towers in the point cloud; and determining sag of the power transmission wire through the distance between the sampling points on the connecting lines of the two hanging points and the power transmission wire point cloud.
According to the method, the device, the computer equipment, the storage medium and the computer program product for measuring the sag of the power transmission wire based on the point cloud analysis, the point cloud of the power transmission line is obtained, the power transmission line at least comprises two adjacent towers and the power transmission wire hung on the two towers, the point cloud is subjected to semantic segmentation processing by utilizing a pre-built point cloud semantic segmentation model to obtain a semantic segmentation result of the point cloud, the semantic segmentation result is used for representing the power transmission wire point cloud corresponding to the power transmission wire in the point cloud, the principal component analysis processing is performed on the power transmission wire point cloud according to the semantic segmentation result, two hanging points of the power transmission wire in the point cloud on the two adjacent towers are determined, and the sag of the power transmission wire is determined by the distance between the sampling points on the connecting lines of the two hanging points and the power transmission wire point cloud. According to the scheme, point clouds corresponding to two adjacent towers and power transmission wires hung on the two towers are obtained, semantic segmentation processing is carried out on the point clouds, point clouds corresponding to all objects in the point clouds are determined, principal component analysis processing is carried out on the power transmission wire point clouds, two hanging points of the power transmission wires on the two adjacent towers in the point clouds are identified, sag of the power transmission wires is automatically determined according to the distance between sampling points on connecting lines of the two hanging points and the power transmission wire point clouds, and accordingly accuracy and efficiency of determining sag of the power transmission wires are improved.
Drawings
FIG. 1 is a flow chart of a method for sag measurement of a power transmission line based on point cloud analysis in one embodiment;
FIG. 2 is a schematic diagram of a point cloud semantic segmentation model in one embodiment;
FIG. 3 is a schematic diagram of a point cloud attention mechanism module in one embodiment;
FIG. 4 is a block diagram of a power transmission conductor sag measurement device based on point cloud analysis in one embodiment;
fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, a method for measuring sag of a power transmission wire based on point cloud analysis is provided, and the embodiment is applied to a terminal for illustration by the method, and includes the following steps:
step S101, obtaining point clouds of a power transmission line.
In this step, the transmission line includes at least two adjacent towers and transmission wires suspended on the two towers.
Specifically, the terminal determines a power transmission line of which sag is to be measured, and acquires a point cloud of the power transmission line.
Step S102, carrying out semantic segmentation processing on the point cloud by utilizing a pre-constructed point cloud semantic segmentation model to obtain a semantic segmentation result of the point cloud.
In this step, as shown in fig. 2, the point cloud semantic segmentation model may be a neural network model for point cloud semantic segmentation; the semantic segmentation result is used for representing a power transmission wire point cloud corresponding to the power transmission wire in the point cloud.
Specifically, the terminal inputs the point cloud into a pre-constructed point cloud semantic segmentation model, performs semantic segmentation processing on the point cloud through the pre-constructed point cloud semantic segmentation model, and obtains semantic segmentation results for all objects (such as towers, wires, ground, vegetation and buildings) in the point cloud as semantic segmentation results of the point cloud.
And step S103, carrying out principal component analysis processing on the point cloud of the power transmission wire according to the semantic segmentation result, and determining two hanging points of the power transmission wire on two adjacent towers in the point cloud.
In this step, the two suspension points may be two suspension points in which the power transmission wire is suspended on two adjacent towers in the point cloud.
Specifically, the terminal performs principal component analysis processing on the power transmission wire point cloud according to the semantic segmentation result, and determines two suspension points of the power transmission wire on two adjacent towers in the point cloud.
Step S104, determining sag of the transmission line through the distance between the sampling points on the connecting line of the two hanging points and the point cloud of the transmission line.
In this step, the distance may be a vertical distance.
Specifically, the terminal determines sag of the power transmission wire by identifying or calculating the distance between the sampling point on the connecting line of the two suspension points and the power transmission wire point cloud.
In the method for measuring sag of the power transmission wire based on the point cloud analysis, the point cloud of the power transmission line is obtained, the power transmission line at least comprises two adjacent towers and the power transmission wire hung on the two towers, semantic segmentation processing is carried out on the point cloud by utilizing a pre-built point cloud semantic segmentation model to obtain a semantic segmentation result of the point cloud, the semantic segmentation result is used for representing the power transmission wire point cloud corresponding to the power transmission wire in the point cloud, main component analysis processing is carried out on the power transmission wire point cloud according to the semantic segmentation result, two hanging points of the power transmission wire on the two adjacent towers in the point cloud are determined, and sag of the power transmission wire is determined through the distance between sampling points on the connecting line of the two hanging points and the power transmission wire point cloud. According to the scheme, point clouds corresponding to two adjacent towers and power transmission wires hung on the two towers are obtained, semantic segmentation processing is carried out on the point clouds, the point clouds corresponding to all objects in the point clouds are determined, principal component analysis processing is carried out on the power transmission wire point clouds, two hanging points of the power transmission wires on the two adjacent towers in the point clouds are identified, sag of the power transmission wires is automatically determined according to the distance between sampling points on connecting lines of the two hanging points and the power transmission wire point clouds, and therefore accuracy and efficiency of determining sag of the power transmission wires are improved.
In one embodiment, according to the semantic segmentation result in the step S103, performing principal component analysis processing on the power transmission wire point cloud, and determining two suspension points of the power transmission wire on two adjacent towers in the point cloud specifically includes: according to the semantic segmentation result, carrying out principal component analysis processing on the power transmission wire point cloud to obtain a principal direction vector of the power transmission wire point cloud; and determining two suspension points of the power transmission wire on two adjacent towers in the point cloud according to the coordinate information of the main direction vector of the power transmission wire point cloud.
In this embodiment, the coordinate information may be coordinates (coordinate positions).
Specifically, the terminal analyzes and processes principal components of the power transmission wire point cloud according to semantic segmentation results to obtain a principal direction vector of the power transmission wire point cloud, and determines coordinate positions of two hanging points of the power transmission wire on two adjacent towers in the point cloud according to coordinate information of the principal direction vector of the power transmission wire point cloud in the point cloud.
According to the technical scheme, the two hanging points of the power transmission wire on the two adjacent towers in the point cloud are determined by carrying out principal component analysis processing on the power transmission wire point cloud, so that the two hanging points can be accurately determined, and the accuracy of determining sag of the power transmission wire can be improved.
In one embodiment, the method may further determine a principal direction vector of the power transmission wire point cloud by the following steps, specifically including: according to the semantic segmentation result, calculating a covariance matrix corresponding to the point cloud of the transmission line; and determining a main direction vector of the point cloud of the power transmission wire by carrying out singular value decomposition processing on the covariance matrix.
In this embodiment, the principal component analysis processing may include a calculation covariance matrix processing and a singular value decomposition processing.
Specifically, the terminal calculates a covariance matrix corresponding to the power transmission wire point cloud according to the semantic segmentation result, and determines a main direction vector of the power transmission wire point cloud by performing singular value decomposition processing on the covariance matrix.
According to the technical scheme, the main direction vector of the power transmission wire point cloud is obtained accurately through the covariance matrix processing and the singular value decomposition processing, so that the accuracy of determining the sag of the power transmission wire is improved.
In one embodiment, the semantic segmentation processing is performed on the point cloud by using the pre-constructed point cloud semantic segmentation model in step S102, and the obtaining the semantic segmentation result of the point cloud specifically includes: enhancing the connection feature in the point cloud by using a point cloud attention mechanism unit in the point cloud semantic segmentation model; and carrying out semantic segmentation processing on the point cloud according to the features of the connection part to obtain a semantic segmentation result.
In this embodiment, as shown in fig. 2 and 3, the point cloud attention mechanism unit may be a point cloud attention mechanism module.
Specifically, the terminal utilizes a point cloud attention mechanism unit in a point cloud semantic segmentation model to strengthen the characteristics of the joints in the point cloud, and performs semantic segmentation processing on the point cloud according to the enhanced characteristics of the joints to obtain a semantic segmentation result.
According to the technical scheme, the semantic segmentation processing is carried out on the point cloud by enhancing the characteristics of the joint in the point cloud, so that the semantic segmentation result can be obtained more accurately and faster, and the accuracy and the efficiency of determining the sag of the power transmission wire can be improved.
In one embodiment, the semantic segmentation processing is performed on the point cloud by using the pre-constructed point cloud semantic segmentation model in step S102, and the obtaining the semantic segmentation result of the point cloud specifically includes: carrying out semantic segmentation processing on the point cloud by utilizing a point cloud semantic segmentation model, and dividing the point cloud into a transmission wire point cloud, a tower point cloud, a ground point cloud, a vegetation point cloud and a building point cloud; and determining a semantic segmentation result according to the transmission wire point cloud, the tower point cloud, the ground point cloud, the vegetation point cloud and the building point cloud.
In this embodiment, the power transmission line includes two towers, a power transmission wire, ground, vegetation and a building; the tower point cloud may represent a tower point cloud corresponding to a tower among the point clouds; the ground point cloud may represent a ground point cloud corresponding to the ground among the point clouds; the vegetation point cloud can represent a vegetation point cloud corresponding to vegetation in the point cloud; the building point cloud may represent a building point cloud corresponding to a building from among the point clouds.
Specifically, the terminal performs semantic segmentation processing on point clouds by using a point cloud semantic segmentation model, divides the point clouds into a transmission wire point cloud, a pole tower point cloud, a ground point cloud, a vegetation point cloud and a building point cloud, and determines semantic segmentation results according to the transmission wire point cloud, the pole tower point cloud, the ground point cloud, the vegetation point cloud and the building point cloud.
According to the technical scheme, the point cloud is divided into the power transmission wire point cloud, the tower point cloud, the ground point cloud, the vegetation point cloud and the building point cloud, and the power transmission wire point cloud, the vegetation point cloud and the building point cloud are determined to be semantic segmentation results, so that the semantic segmentation results can be obtained faster and more accurately, and the accuracy and the efficiency for determining sag of the power transmission wire can be improved subsequently.
In one embodiment, determining the sag of the power transmission line in step S104 includes: identifying the distance between the sampling point and the power transmission wire point cloud to obtain a distance set; and selecting the maximum distance from the distance set as the sag of the power transmission wire.
In this embodiment, the distance set represents a set of distances between the plurality of sampling points and the power transmission wire point cloud.
Specifically, the terminal identifies the distance between the sampling point and the power transmission wire point cloud to obtain a distance set, and the maximum distance is selected from the distance set to serve as sag of the power transmission wire.
According to the technical scheme, the maximum distance among the distances between each sampling point and the point cloud of the power transmission wire is used as the sag of the power transmission wire, so that the accuracy of determining the sag of the power transmission wire is improved.
In one embodiment, the method may further obtain a distance set by the following steps, specifically including: according to the preset interval, a plurality of sampling points are determined on the connecting line of the two hanging points; determining intersection points of extension lines of all sampling points along a ground normal vector and the point cloud of the transmission wire; and calculating the distance between each sampling point and the corresponding intersection point to obtain a distance set.
In this embodiment, the preset interval may represent a preset interval on the connecting line of the two suspension points; the connecting line of the two hanging points can be a straight line connecting the two hanging points; the extension line of the sampling point along the ground normal vector may be the extension line of the sampling point along the vertical ground direction.
Specifically, the terminal determines a plurality of sampling points on a connecting line of two suspension points according to a preset interval, determines intersection points of extension lines of the ground normal vector of all the sampling points and the power transmission wire point cloud, calculates the distance between each sampling point and the corresponding intersection point, and obtains a distance set.
According to the technical scheme, after the plurality of sampling points are determined on the connecting lines of the two hanging points according to the preset intervals, the distance between each sampling point and the corresponding intersection point on the power transmission wire point cloud is calculated, so that a distance set is obtained, more accurate distance sets are obtained, and the accuracy of determining sag of the power transmission wire is improved.
The following describes, in an embodiment, a method for measuring sag of a power transmission wire based on point cloud analysis provided in the present application, where the method is applied to a terminal for illustration, and the main steps include:
the method comprises the first step that a terminal obtains point clouds of a power transmission line.
And secondly, the terminal utilizes a point cloud attention mechanism unit in the point cloud semantic segmentation model to enhance the connection feature in the point cloud.
And thirdly, the terminal performs semantic segmentation processing on the point cloud according to the characteristics of the connection part, and divides the point cloud into a transmission wire point cloud, a pole tower point cloud, a ground point cloud, a vegetation point cloud and a building point cloud.
Fourth, the terminal determines semantic segmentation results according to the transmission wire point cloud, the tower point cloud, the ground point cloud, the vegetation point cloud and the building point cloud.
And fifthly, the terminal calculates a covariance matrix corresponding to the power transmission wire point cloud according to the semantic segmentation result.
And sixthly, the terminal determines a main direction vector of the point cloud of the transmission line by carrying out singular value decomposition processing on the covariance matrix.
And seventhly, the terminal determines two hanging points of the power transmission wire on two adjacent towers in the point cloud according to the coordinate information of the main direction vector of the power transmission wire point cloud.
Eighth step, the terminal determines a plurality of sampling points on the connecting line of the two hanging points according to the preset interval.
And ninth, determining the intersection point of the extension line of each sampling point along the ground normal vector and the power transmission wire point cloud by the terminal.
And tenth, the terminal calculates the distance between each sampling point and the corresponding intersection point to obtain a distance set.
Eleventh step, the terminal selects the maximum distance from the distance set as the sag of the transmission line.
The power transmission line at least comprises two adjacent towers and a power transmission wire hung on the two towers; the power transmission line comprises two towers, a power transmission wire, the ground, vegetation and a building.
According to the technical scheme, semantic segmentation processing is carried out on point clouds by acquiring point clouds corresponding to two adjacent towers and power transmission wires hung on the two towers, the point clouds corresponding to all objects in the point clouds are determined, principal component analysis processing is carried out on the power transmission wire point clouds, two hanging points of the power transmission wires on the two adjacent towers in the point clouds are identified, sag of the power transmission wires is automatically determined according to the distance between sampling points on connecting lines of the two hanging points and the power transmission wire point clouds, and accordingly accuracy and efficiency of determining sag of the power transmission wires are improved.
The method for measuring sag of the power transmission wire based on the point cloud analysis is described by using an application example, and the application example is applied to a terminal for illustration by using the method, and the main steps include:
firstly, a terminal performs semantic segmentation of the point cloud of the power transmission line: the terminal realizes 5 kinds of point cloud semantic segmentation of the transmission line towers, wires, the ground, vegetation and buildings by designing a point cloud semantic segmentation neural network (a point cloud semantic segmentation model).
The purpose of the semantic segmentation of the point cloud is to realize the sag measurement of the wire, so that the connection position of two types of point clouds of a pole tower and a wire is more concerned when the network is designed; in order to enhance the segmentation effect of the junction of the pole tower and the wire point cloud, a point cloud attention mechanism module is designed, and the feature extraction capacity of the junction is enhanced by calculating the weighting of the feature information of the adjacent point clouds; the point cloud attention mechanism module is used for replacing a multi-layer perceptron module in the PointNet (point net) to extract characteristics, the structure of the point cloud semantic segmentation model is shown in figure 2, and the point cloud semantic segmentation model comprises n×3, rotation transformation, n×6, point cloud attention mechanism modules, n×64, n×1024, n×1088, n×7 and other processing processes, wherein n can represent point clouds or points; the point cloud attention mechanism module is shown in fig. 3 and includes information of α, β, γ, δ, ε, a, b, c, d, e, f, and the like, where f=a×γ+b×α+c×β+d×ε+e×δ.
Secondly, detecting a wire hanging point by the terminal: the terminal calculates a principal direction vector of the wire point cloud through a principal component analysis method based on the wire point cloud semantic segmentation result obtained in the first step, and firstly calculates a covariance matrix of the wire point cloud:
wherein N is the number of wire point clouds, p i The numerical value of the point cloud coordinate in three directions of XYZ (coordinate axis), p m Is the average value of the point cloud coordinates in three directions of XYZ.
Thirdly, the terminal carries out singular value decomposition on the covariance matrix: m=u Σv T The first value of the diagonal line of the matrix with the sigma of 3*3 is the main direction characteristic value of the wire, the first column of the matrix with the U of 3*3 is the main direction vector of the wire; and the terminal determines suspension points A and B of the wires on two adjacent base towers according to the coordinate maximum value and the coordinate minimum value of the coordinate axis direction corresponding to the main direction vector of the wires.
Fourthly, the terminal performs wire hanging point connecting wire equidistant sampling: the terminal samples the equal interval distance h (h can be any set length) between the connecting lines AB, the initial interval distance is 1 meter, the distance between the intersection point of each sampling point along the extended line of the ground normal vector N and the lead is calculated, and the distance is the candidate sag.
Fifthly, the terminal performs equidistant sampling on the largest candidate sag neighborhood: and the terminal selects the maximum value in the candidate sag calculated in the fourth step, namely the maximum subsequent sag, and samples at equal intervals within the range of h/2 meters at the two sides of the sampling point of the maximum subsequent sag, and calculates the distance between each sampling point and the intersection point of the lead along the extension line of the ground normal vector N. This step is repeated until the separation distance is h/16, and the maximum candidate sag at this time is the actual sag value.
According to the technical scheme of the application example, the point cloud semantic segmentation neural network is used for conducting semantic segmentation on the point cloud of the power transmission line, then conducting wire hanging point detection is conducted through a principal component analysis method according to the point cloud semantic segmentation result output by the network, finally the wire hanging point connecting line is subjected to equal interval sampling and sag calculation, the maximum sag of the wire is calculated in an iterative mode, and accuracy and efficiency of determining sag of the power transmission wire are improved.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a power transmission wire sag measurement device based on the point cloud analysis, which is used for realizing the power transmission wire sag measurement method based on the point cloud analysis. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the one or more power transmission wire sag measurement devices based on the point cloud analysis provided below may be referred to the limitation of the power transmission wire sag measurement method based on the point cloud analysis hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 4, a power transmission wire sag measurement device based on point cloud analysis is provided, and the device 400 may include:
a point cloud acquisition module 401, configured to acquire a point cloud of a power transmission line; the power transmission line at least comprises two adjacent towers and a power transmission wire suspended on the two towers;
the result obtaining module 402 is configured to perform semantic segmentation processing on the point cloud by using a pre-constructed point cloud semantic segmentation model, so as to obtain a semantic segmentation result of the point cloud; the semantic segmentation result is used for representing a power transmission wire point cloud corresponding to the power transmission wire in the point cloud;
the point cloud analysis module 403 is configured to perform principal component analysis processing on the power transmission wire point cloud according to the semantic segmentation result, and determine two suspension points of the power transmission wire on the two adjacent towers in the point cloud;
and the sag determining module 404 is configured to determine sag of the power transmission wire according to a distance between the sampling point on the connecting line of the two suspension points and the power transmission wire point cloud.
In one embodiment, the point cloud analysis module 403 is further configured to perform principal component analysis processing on the power transmission wire point cloud according to the semantic segmentation result, to obtain a principal direction vector of the power transmission wire point cloud; and determining two suspension points of the power transmission wire on the two adjacent towers in the point cloud according to the coordinate information of the main direction vector of the power transmission wire point cloud.
In one embodiment, the point cloud analysis module 403 is further configured to calculate a covariance matrix corresponding to the power transmission wire point cloud according to the semantic segmentation result; and determining a main direction vector of the power transmission wire point cloud by carrying out singular value decomposition processing on the covariance matrix.
In one embodiment, the result obtaining module 402 is further configured to enhance a feature of a connection in the point cloud by using a point cloud attention mechanism unit in the point cloud semantic segmentation model; and carrying out semantic segmentation processing on the point cloud according to the features of the connecting positions to obtain the semantic segmentation result.
In one embodiment, the transmission line includes the two towers, the transmission line, ground, vegetation, and a building; the result obtaining module 402 is further configured to perform semantic segmentation processing on the point cloud by using the point cloud semantic segmentation model, and divide the point cloud into the transmission line point cloud, the tower point cloud, the ground point cloud, the vegetation point cloud and the building point cloud; and determining the semantic segmentation result according to the transmission wire point cloud, the tower point cloud, the ground point cloud, the vegetation point cloud and the building point cloud.
In one embodiment, the sag determining module 404 is further configured to identify a distance between the sampling point and the power transmission line point cloud, to obtain a distance set; and selecting the largest distance from the distance set as the sag of the power transmission wire.
In one embodiment, the apparatus 400 further comprises: the sampling point determining module is used for determining a plurality of sampling points on the connecting line of the two hanging points according to a preset interval; the sag determining module 404 is further configured to determine an intersection point of an extension line of the ground normal vector of each sampling point and the power transmission wire point cloud; and calculating the distance between each sampling point and the corresponding intersection point to obtain the distance set.
The modules in the power transmission wire sag measurement device based on the point cloud analysis can be all or partially realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program when executed by a processor is used for realizing a method for measuring sag of a power transmission wire based on point cloud analysis. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to comply with the related laws and regulations and standards of the related countries and regions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as Static Random access memory (Static Random access memory AccessMemory, SRAM) or dynamic Random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method for measuring sag of a power transmission wire based on point cloud analysis, the method comprising:
acquiring a point cloud of a power transmission line; the power transmission line at least comprises two adjacent towers and a power transmission wire suspended on the two towers;
performing semantic segmentation processing on the point cloud by utilizing a pre-constructed point cloud semantic segmentation model to obtain a semantic segmentation result of the point cloud; the semantic segmentation result is used for representing a power transmission wire point cloud corresponding to the power transmission wire in the point cloud;
according to the semantic segmentation result, carrying out principal component analysis processing on the power transmission wire point cloud, and determining two hanging points of the power transmission wire on the two adjacent towers in the point cloud;
and determining sag of the power transmission wire through the distance between the sampling point on the connecting line of the two hanging points and the power transmission wire point cloud.
2. The method according to claim 1, wherein the performing principal component analysis processing on the power transmission line point cloud according to the semantic segmentation result, determining two hanging points of the power transmission line on the two adjacent towers in the point cloud includes:
according to the semantic segmentation result, carrying out principal component analysis processing on the power transmission wire point cloud to obtain a principal direction vector of the power transmission wire point cloud;
and determining two suspension points of the power transmission wire on the two adjacent towers in the point cloud according to the coordinate information of the main direction vector of the power transmission wire point cloud.
3. The method according to claim 2, wherein the performing principal component analysis processing on the power transmission wire point cloud according to the semantic segmentation result to obtain a principal direction vector of the power transmission wire point cloud includes:
according to the semantic segmentation result, calculating a covariance matrix corresponding to the power transmission wire point cloud;
and determining a main direction vector of the power transmission wire point cloud by carrying out singular value decomposition processing on the covariance matrix.
4. The method of claim 1, wherein the performing semantic segmentation processing on the point cloud by using a pre-constructed point cloud semantic segmentation model to obtain a semantic segmentation result of the point cloud comprises:
enhancing the connection feature in the point cloud by using a point cloud attention mechanism unit in the point cloud semantic segmentation model;
and carrying out semantic segmentation processing on the point cloud according to the features of the connecting positions to obtain the semantic segmentation result.
5. The method of claim 1, wherein the power transmission line comprises the two towers, the power transmission line, ground, vegetation, and a building;
the semantic segmentation processing is carried out on the point cloud by utilizing a pre-constructed point cloud semantic segmentation model to obtain a semantic segmentation result of the point cloud, and the semantic segmentation method comprises the following steps:
performing semantic segmentation processing on the point cloud by using the point cloud semantic segmentation model, and dividing the point cloud into the transmission wire point cloud, the tower point cloud, the ground point cloud, the vegetation point cloud and the building point cloud;
and determining the semantic segmentation result according to the transmission wire point cloud, the tower point cloud, the ground point cloud, the vegetation point cloud and the building point cloud.
6. The method of claim 1, wherein determining sag of the power conductor from a distance between the sampling point on the line connecting the two suspension points and the power conductor point cloud comprises:
identifying the distance between the sampling point and the power transmission wire point cloud to obtain a distance set;
and selecting the largest distance from the distance set as the sag of the power transmission wire.
7. The method of claim 6, further comprising, prior to identifying the distance between the sampling point and the power conductor point cloud to obtain a set of distances:
determining a plurality of sampling points on a connecting line of the two hanging points according to a preset interval;
the calculating the distance between the sampling point and the power transmission wire point cloud to obtain a distance set comprises the following steps:
determining intersection points of the extension lines of the sampling points along the ground normal vector and the power transmission wire point cloud;
and calculating the distance between each sampling point and the corresponding intersection point to obtain the distance set.
8. A power transmission conductor sag measurement device based on point cloud analysis, the device comprising:
the point cloud acquisition module is used for acquiring the point cloud of the power transmission line; the power transmission line at least comprises two adjacent towers and a power transmission wire suspended on the two towers;
the result obtaining module is used for carrying out semantic segmentation processing on the point cloud by utilizing a pre-constructed point cloud semantic segmentation model to obtain a semantic segmentation result of the point cloud; the semantic segmentation result is used for representing a power transmission wire point cloud corresponding to the power transmission wire in the point cloud;
the point cloud analysis module is used for carrying out principal component analysis processing on the point cloud of the power transmission wire according to the semantic segmentation result and determining two hanging points of the power transmission wire on the two adjacent towers in the point cloud;
and the sag determination module is used for determining sag of the power transmission wire through the distance between the sampling point on the connecting line of the two hanging points and the power transmission wire point cloud.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310051465.9A CN116128835B (en) | 2023-02-02 | 2023-02-02 | Point cloud analysis-based power transmission wire sag measurement method, device and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310051465.9A CN116128835B (en) | 2023-02-02 | 2023-02-02 | Point cloud analysis-based power transmission wire sag measurement method, device and equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116128835A true CN116128835A (en) | 2023-05-16 |
| CN116128835B CN116128835B (en) | 2023-09-15 |
Family
ID=86300594
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310051465.9A Active CN116128835B (en) | 2023-02-02 | 2023-02-02 | Point cloud analysis-based power transmission wire sag measurement method, device and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116128835B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102230784A (en) * | 2011-04-18 | 2011-11-02 | 四川电力科学研究院 | Wire detection device and detection method |
| CN111798475A (en) * | 2020-05-29 | 2020-10-20 | 浙江工业大学 | Indoor environment 3D semantic map construction method based on point cloud deep learning |
| CN112052884A (en) * | 2020-08-17 | 2020-12-08 | 南京审计大学 | Point cloud classification method and system based on local edge feature enhancement |
| CN113345094A (en) * | 2021-05-18 | 2021-09-03 | 武汉大学 | Electric power corridor safety distance analysis method and system based on three-dimensional point cloud |
| CN113870123A (en) * | 2021-08-19 | 2021-12-31 | 中国铁路设计集团有限公司 | Automatic detection method for contact net leading height and pulling value based on vehicle-mounted mobile laser point cloud |
| CN114170410A (en) * | 2021-11-30 | 2022-03-11 | 西安理工大学 | Point cloud part level segmentation method based on PointNet graph convolution and KNN search |
| CN114972631A (en) * | 2022-04-21 | 2022-08-30 | 国网江苏省电力有限公司南京供电分公司 | Online reconstruction method and system for laser point cloud model of power transmission line |
-
2023
- 2023-02-02 CN CN202310051465.9A patent/CN116128835B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102230784A (en) * | 2011-04-18 | 2011-11-02 | 四川电力科学研究院 | Wire detection device and detection method |
| CN111798475A (en) * | 2020-05-29 | 2020-10-20 | 浙江工业大学 | Indoor environment 3D semantic map construction method based on point cloud deep learning |
| CN112052884A (en) * | 2020-08-17 | 2020-12-08 | 南京审计大学 | Point cloud classification method and system based on local edge feature enhancement |
| CN113345094A (en) * | 2021-05-18 | 2021-09-03 | 武汉大学 | Electric power corridor safety distance analysis method and system based on three-dimensional point cloud |
| CN113870123A (en) * | 2021-08-19 | 2021-12-31 | 中国铁路设计集团有限公司 | Automatic detection method for contact net leading height and pulling value based on vehicle-mounted mobile laser point cloud |
| CN114170410A (en) * | 2021-11-30 | 2022-03-11 | 西安理工大学 | Point cloud part level segmentation method based on PointNet graph convolution and KNN search |
| CN114972631A (en) * | 2022-04-21 | 2022-08-30 | 国网江苏省电力有限公司南京供电分公司 | Online reconstruction method and system for laser point cloud model of power transmission line |
Non-Patent Citations (1)
| Title |
|---|
| 王礼田 等: "基于双目视觉稀疏点云重建的输电线路弧垂测量方法", 太原理工大学学报, vol. 47, no. 6, pages 747 - 751 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116128835B (en) | 2023-09-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107767375B (en) | Point cloud quality evaluation method and device | |
| CN112800915B (en) | Building change detection method, device, electronic equipment and storage medium | |
| CN112102432B (en) | Method and device for drawing air quality vertical distribution diagram and storage medium | |
| CN106777302B (en) | Method and device for converting space geographic coordinates | |
| CN111884207B (en) | Power grid topological structure visualization method, system and medium based on electrical distance | |
| CN107729582A (en) | Component defect inspection and forecasting system based on TLS | |
| CN115659985A (en) | Electric power knowledge graph entity alignment method and device and computer equipment | |
| CN115081249A (en) | Method and device for determining scale of reactor test model and computer equipment | |
| CN113239815B (en) | Remote sensing image classification method, device and equipment based on real semantic full-network learning | |
| CN117631682A (en) | Method and system for determining inspection route of unmanned aerial vehicle of power grid | |
| CN116128835B (en) | Point cloud analysis-based power transmission wire sag measurement method, device and equipment | |
| CN116029151B (en) | Water flow resistance prediction method, training method, flow prediction method and device | |
| CN116702571A (en) | Numerical simulation method and device based on multiple smoothness measurement factors | |
| CN115908318A (en) | Power line sag determination method, device, equipment, medium and product | |
| CN116883563A (en) | Method, device, computer equipment and storage medium for rendering annotation points | |
| CN115830073A (en) | Map element reconstruction method, map element reconstruction device, computer equipment and storage medium | |
| CN115810012B (en) | Transmission tower inclination detection method, device, equipment and storage medium | |
| CN114329868A (en) | Substation equipment safety distance checking method and device and computer equipment | |
| CN113159426A (en) | Weather type similarity judgment method and device, electronic equipment and readable storage medium | |
| CN113487634A (en) | Method and device for correlating height and area of building | |
| CN115240401B (en) | Vehicle position determining method, device, equipment, medium and product | |
| CN112507596A (en) | Method and device for analyzing groundwater level, electronic device and storage medium | |
| CN116718122B (en) | Sag measurement method and device based on image parallax depth and computer equipment | |
| CN115290927B (en) | Runway wind component correction method, runway wind component correction device, computer equipment and storage medium | |
| CN116229271A (en) | Image difference detection method, apparatus, device, storage medium, and program product |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address |
Address after: Room 86, room 406, No.1, Yichuang street, Zhongxin Guangzhou Knowledge City, Huangpu District, Guangzhou City, Guangdong Province Patentee after: Southern Power Grid Digital Grid Research Institute Co.,Ltd. Country or region after: China Address before: Room 86, room 406, No.1, Yichuang street, Zhongxin Guangzhou Knowledge City, Huangpu District, Guangzhou City, Guangdong Province Patentee before: Southern Power Grid Digital Grid Research Institute Co.,Ltd. Country or region before: China |